Machine learning techniques can be used to predict an output value (formally, a dependent variable value) from a plurality of input values (formally, independent variable values). Such a machine learning model, often referred to as a supervisory learning model, may be trained with a data set that maps combinations of input values to known output values. Once trained, the model can be applied to further inputs in order to predict the corresponding output values. But these conventional machine learning models cannot be used to predict which ranges of input values may result in a particular output value.